The goal of this research is to correlate Raman spectra with tissue pathology using principal component analysis. Principal component analysis enables us to reduce our data set into a smaller number of mathematical lineshapes, called principal components, which represent the spectral variations in the data. A linear combination of these principal components can then be used to fit individual data. Finally, logisitic regression is used to correlate fitting coefficients with disease classifications and develop a diagnostic algorithm. So far, we have developed an initial decision algorithm and are in the process of testing it on data not included in the original data set.